Journal
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS 2017)
Volume 108, Issue -, Pages 1743-1752Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2017.05.019
Keywords
Few-body problems; latent matrices; QR factorization; task-parallelism; multicore CPUs
Categories
Funding
- MINECO [TIN2014-53495-R]
- FEDER
- UJI [P1-1B2015-26]
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We re-formulate a classical numerical method for the solution of systems of linear equations to tackle problems with latent data, that is, linear systems of dimension that is a priori unknown. This type of systems appears in the solution of few-body Coulomb problems for Atomic Simulation Physics, in the form of multidimensional partial differential equations (PDEs) that require the numerical solution of a sequence of recurrent dense linear systems of growing scale. The large dimension of these systems, with up to several hundred thousands of unknowns, is tackled in our approach via a task-parallel implementation of a solver based on the QR factorization. This method is parallelized using the OmpSs framework, showing fair strong and weak scalability on a multicore processor equipped with 12 Intel cores. (C) 2017 The Authors. Published by Elsevier B.V.
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